ArticleProceedings of the National Academy of Sciences of the United States of America2022
Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis.Communications chemistry · 2026Article
- Leveraging Molecular Mechanics With the uESE Continuum Solvation Model for Efficient Solvation Free Energy Prediction: Impact of Conformation and Extensive Validation.Journal of computational chemistry · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Dynamic Collision Fingerprints (DCF): Introducing a New Descriptor Linking Lattice Interactions to 2D Structural Data Signatures.Journal of chemical theory and computation · 2025Article
- Infrared spectroscopy-based zero-shot learning for identifying reaction intermediates in unseen systems.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling.Journal of chemical information and modeling · 2025Article
- CaProtein science : a publication of the Protein Society · 2025Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Article
- Deep Kernel learning for reaction outcome prediction and optimization.Communications chemistry · 2024Article
- A robotic AI-Chemist system for multi-modal AI-ready database.National science review · 2023Article
- Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center.Research (Washington, D.C.) · 2023Article
- SMG-BERT: integrating stereoscopic information and chemical representation for molecular property prediction.Frontiers in molecular biosciences · 2023Article
- Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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Abstract
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.
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